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Abstract #4276

Cardiac landmark localization on axial stacks without dedicated ground truth

Gaspar Delso1, Eman Ali2, Jane Names2, Dan Rettmann2, and Martin Janich3
1GE HealthCare, Barcelona, Spain, 2GE HealthCare, Waukesha, WI, United States, 3GE HealthCare, Munich, Germany

Synopsis

Keywords: AI/ML Software, Cardiovascular, Workflow

Motivation: In cardiac magnetic resonance imaging, accurately identifying anatomical landmarks is crucial for correctly prescribing the standard views needed to navigate the anatomy.

Goal(s): This study introduces a novel deep learning training approach for cardiac magnetic resonance imaging that reduces dependence on manual annotations.

Approach: By leveraging outputs from pre-trained long-axis models as surrogate ground truth, the method simplifies database creation and maintenance for training networks.

Results: Tested on 578 exams and validated on 100 clinical cases, the model achieved comparable accuracy to manually trained models, with minor deviations mainly due to complex pathologies.

Impact: The proposed approach eliminates the need for manual annotations for the training of some cardiac models. Using outputs from existing long-axis models as surrogate ground truth simplifies the creation and maintenance of the training database.

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Keywords